A Novel Spark-based Attribute Reduction and Neighborhood Classification for Rough Evidence

Neighborhood classification (NEC) algorithms have been widely used to solve classification problems. Most traditional NEC algorithms employ the majority voting mechanism as the basis for final decision making. However, this mechanism hardly considers the spatial difference and label uncertainty of the neighborhood samples, which may increase the possibility of the misclassification. In addition, the traditional NEC algorithms need to load the entire data into memory at once, which is computationally inefficient when the size of the dataset is large. To address these problems, we propose a novel Spark-based attribute reduction and NEC for rough evidence in this article. Specifically, we first construct a multigranular sample space using the parallel undersampling method. Then, we evaluate the significance of attribute by neighborhood rough evidence decision error rate and remove the redundant attribute on different samples subspaces. Based on this attribute reduction algorithm, we design a parallel attribute reduction algorithm which is able to compute equivalence classes in parallel and parallelize the process of searching for candidate attributes. Finally, we introduce the rough evidence into the classification decision of traditional NEC algorithms and parallelize the classification decision process. Furthermore, the proposed algorithms are conducted in the Spark parallel computing framework. Experimental results on both small and large-scale datasets show that the proposed algorithms outperform the benchmarking algorithms in the classification accuracy and the computational efficiency

Cascaded two-stage feature clustering and selection via separability and consistency in fuzzy decision systems

Feature selection is a vital technique in machine learning, as it can reduce computational complexity, improve model performance, and mitigate the risk of overfitting. However, the increasing complexity and dimensionality of datasets pose significant challenges in the selection of features. Focusing on these challenges, this paper proposes a cascaded two-stage feature clustering and selection algorithm for fuzzy decision systems. In the first stage, we reduce the search space by clustering relevant features and addressing inter-feature redundancy. In the second stage, a clustering-based sequentially forward selection method that explores the global and local structure of data is presented. We propose a novel metric for assessing the significance of features, which considers both global separability and local consistency. Global separability measures the degree of intra-class cohesion and inter-class separation based on fuzzy membership, providing a comprehensive understanding of data separability. Meanwhile, local consistency leverages the fuzzy neighborhood rough set model to capture uncertainty and fuzziness in the data. The effectiveness of our proposed algorithm is evaluated through experiments conducted on 18 public datasets and a real-world schizophrenia dataset. The experiment results demonstrate our algorithm\u27s superiority over benchmarking algorithms in both classification accuracy and the number of selected features.This paper has been accepted by IEEE Transactions on Fuzzy Systems for publication. Permission from IEEE must be obtained for all other uses, in any current or future media. The final version is available at [10.1109/TFUZZ.2024.3420963

A Neighborhood Rough Classification with Dempster-Shafer Evidence Theory

In order to further improve the classification mechanism and the performance of neighborhood classifier, a Dempster-Shafer(D-S) evidence-driven neighborhood rough classification method is proposed. Firstly, in attribute reduction, the error rate of neighborhood decision is used as the index of attribute significance, and the attribute reduction method based on neighborhood decision error rate is studied. By removing redundant attributes, an important set of attributes is provided for classification learning. Then, in terms of classifier design, the traditional majority voting mechanism is revised, D-S evidence theory is introduced into the information fusion of neighborhood samples,and a neighborhood classifier based on D-S evidence theory is proposed. Experimental results on UCI public data set show that the proposed method has higher classification accuracy than the neighborhood classifier under the majority voting mechanism. The paper provides a new insight for the further study of neighborhood classification methods

A shared value network model based on synergy theory in intelligent manufacturing

With the in-depth development and integration of science and technology, intelligent manufacturing has gradually become a trend in the manufacturing industry. However, it is difficult for the traditional business model to maximize the use of resources. To address these problems, a shared value network model based on synergy theory is first proposed, which consists of three layers: resource network layer, value connection layer and resource coordination layer. Then, to meet the market demand and maximize the value gain, the optimization scheme is given according to the coordination theory and priority strategy in the design of the operation mechanism of the shared value network model. Finally, the effectiveness of the proposed model is verified by analyzing an example in the actual industry

Multimodal multi-instance evidence fusion neural networks for cancer survival prediction

Abstract Accurate cancer survival prediction plays a crucial role in assisting clinicians in formulating treatment plans. Multimodal data, such as histopathological images, genomic data, and clinical information, provide complementary and comprehensive information, significantly enhancing the accuracy of this task. However, existing methods, despite achieving some promising results, still exhibit two significant limitations: they fail to effectively utilize global context and overlook the uncertainty of different modalities, which may lead to unreliable predictions. In this study, we propose a multimodal multi-instance evidence fusion neural network for cancer survival prediction, called M2EF-NNs. Specifically, to better capture global information from images, we employ a pre-trained vision transformer model to extract patch feature embeddings from histopathological images. Additionally, we are the first to apply the Dempster–Shafer evidence theory to the cancer survival prediction task and introduce subjective logic to estimate the uncertainty of different modalities. We then dynamically adjust the weights of the class probability distribution after multimodal fusion based on the estimated evidence from the fused multimodal data to achieve trusted survival prediction. Finally, the experimental results on three cancer datasets demonstrate that our method significantly improves cancer survival prediction regarding overall C-index and AUC, thereby validating the model’s reliability

δ-Cut Decision-Theoretic Rough Set Approach: Model and Attribute Reductions

Decision-theoretic rough set is a quite useful rough set by introducing the decision cost into probabilistic approximations of the target. However, Yao’s decision-theoretic rough set is based on the classical indiscernibility relation; such a relation may be too strict in many applications. To solve this problem, a δ-cut decision-theoretic rough set is proposed, which is based on the δ-cut quantitative indiscernibility relation. Furthermore, with respect to criterions of decision-monotonicity and cost decreasing, two different algorithms are designed to compute reducts, respectively. The comparisons between these two algorithms show us the following: (1) with respect to the original data set, the reducts based on decision-monotonicity criterion can generate more rules supported by the lower approximation region and less rules supported by the boundary region, and it follows that the uncertainty which comes from boundary region can be decreased; (2) with respect to the reducts based on decision-monotonicity criterion, the reducts based on cost minimum criterion can obtain the lowest decision costs and the largest approximation qualities. This study suggests potential application areas and new research trends concerning rough set theory